在贝叶斯优化中采集采样的高级蒙特卡洛
Javier Garcia-Barcos1, Ruben Martinez-Cantin1
1Instituto Universitario de Investigacion en Ingenieria de Aragon (I3A), Universidad de Zaragoza, 50018 Zaragoza, Spain.
Entropy (Basel, Switzerland)
|January 24, 2025
概括
完全分布式贝叶斯优化 (BO) 增强了复杂系统的优化. 简化的博尔兹曼采样和马尔科夫链蒙特卡洛 (MCMC) 方法提高了采集采样效率,特别是梯度信息.
科学领域:
- 计算科学 计算科学
- 机器学习 机器学习
- 优化算法 优化算法
背景情况:
- 优化复杂系统需要高效的实验选择,往往受到成本和时间的阻碍.
- 贝叶斯优化 (BO) 提供了样本效率,但通常依赖于顺序实验.
- 完全分布式的BO是需要并行/异步搜索,解决隐私和资源限制.
研究的目的:
- 为复杂系统增强完全分布式贝叶斯优化 (BO).
- 解决分布式BO内部采样采集功能的挑战.
- 提高并行和异步主动搜索方法的效率.
主要方法:
- 引入了一个简化的博尔兹曼抽样方法,用于完全分布的BO.
- 分析了各种马尔科夫链蒙特卡洛 (MCMC) 采购采样方法.
- 实现了数值改进的日志预期改进 (EI) 获取功能.
- 将梯度信息纳入MCMC采样方法,如MALA和CyclicalSGLD.
主要成果:
- 梯度知情的MCMC方法 (MALA,CyclicalSGLD) 显著提高了采集采样效率.
- 大都会 - 黑斯廷斯框架内的建议混合证明有效和简单.
- 简化的博尔茨曼抽样方法可促进更高效的分布式BO.
结论:
- 梯度信息对于在分布式BO中增强基于MCMC的采购采样至关重要.
- 简化的博尔兹曼采样与先进的MCMC技术相结合,为并行优化提供了强大的解决方案.
- 提出的方法提高了BO在资源密集型环境中的可扩展性和适用性.
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